1. Field of the Invention
This invention relates generally to printing, and in particular to printing systems that have functionality for classifying an image and performing enhancement processing thereon based on its classification to improve the printed result.
2. Background of the Invention
As digital cameras become more ubiquitous—especially those integrated into cellular phones—they play an increasingly important role in the capture and sharing of visual information in the workplace. In a meeting, for example, an attendee may use a digital camera to capture an image of the contents of a whiteboard, information on a set of slides, a business card, or a scene with other people. Workplace studies have shown that people would use a digital camera in the office to capture these kinds of images if a camera were available. See, e.g., Brown et al., “A diary study of information capture in working life,” Proceedings of ACM CHI 2000 Conference on Human Factors in Computing Systems, vol. 1, p. 438-45 (2000). Accordingly, cameras and other image capture devices present a unique opportunity to increase workplace productivity.
But while many people in the workplace are apt to find uses for capturing images, fundamental limitations remain. For example, office workers generally have little time for classifying and organizing the images they capture. In addition, although capturing images in the workplace is relatively easy, improving those images for printing is not. The average office worker lacks the technical knowledge to apply the appropriate post-processing to an image before printing it to achieve a high quality printout. Moreover, the types of enhancements that should be applied to an image tend to vary depending on the semantic type of the image. For example, a digital picture of a whiteboard would have different enhancement needs than a digital picture of someone's business card. Accordingly, the task of enhancing an image varies by the image type, further complicating the enhancement process.
Some existing systems detect low-level features in images (e.g., contrast, darkness, or color) and automatically apply some type of image processing to improve the images before printing. However, these systems lack the ability to apply more meaningful post-processing to the images that is tailored for the particular semantic class of the image—rather than just on the low-level properties of the image. Because existing systems can at best detect only low-level features of an image, the types of post-processing they can perform is correspondingly limited to basic image processing, which may not be adequate to improve the quality of the image.